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Abstract:

Embodiments of the invention relate to a system, method and apparatus for
performing a multi-source talent acquisition. The method includes
entering search criteria; selecting at least one source from a plurality
of sources; executing a search using at least the search criteria and the
at least one source; identifying at least one talent match; and
displaying the at least one talent match.

Claims:

1. A method for performing a multi-source talent acquisition, the method
comprising: entering search criteria; selecting at least one source from
a plurality of sources; executing a search using at least the search
criteria and the at least one source; identifying at least one talent
match; and displaying the at least one talent match.

2. The method of claim 1, wherein entering the search criteria includes
assigning varying weightage to components of the search criteria.

3. The method of claim 2, further comprising integrating with a position
profile registration system to access and retrieve the search criteria
pertaining to a predefined position.

4. The method of claim 1, further comprising accessing and analyzing
objective and standardizing information on a candidate's capabilities and
executing a contextual information search through at least one resume to
identify and recommend talent.

5. The method of claim 1, further comprising integrating with at least
one assessment system, accessing, retrieving and analyzing relating to a
candidate's performance for evaluating candidature for a position, based
at least on standardized and objective information.

6. The method of claim 5, further comprising analyzing a candidate's
performance across multiple past assessments having relevance to skills
and qualifications embodied in a position profile for which at least a
part of a search is being executed for.

7. The method of claim 1, further comprising searching and evaluating
candidates based on information stored in an interview system.

8. The method of claim 7, further comprising computing a candidate's fit
as it pertains to a specified search criteria utilizing interview
assessment data, taking into account a volume of historical assessment
data available for each candidate and defined weightage for at least one
search term and candidate performance across the at least one question
relevant to the search term, and the complexity of the such question.

9. The method of claim 1, further comprising acquiring resumes using
instructions that monitors arrival of new resumes into a candidate
information system resume repository by a multisource talent acquisition
system and processing the resumes.

10. The method of claim 1, further comprising representing candidates
using graphical objects in a two dimensional space, where candidates with
similar profiles are clustered together.

11. The method of claim 1, operating on a system for computing a total
candidate test score for at least one candidate utilizing parameters, the
system comprising: a memory for storing instructions and data, the data
comprising a set of programs and a dataset having one or more data
fields; and a server that executes the instructions and processes the
data.

12. The method of claim 11, wherein the system further comprises
integrating with a position profile registration system to access and
retrieve the search criteria pertaining to a predefined position.

13. The method of claim 11, wherein the system further comprises
accessing and analyzing objective and standardizing information on a
candidate's capabilities and executing a contextual information search
through at least one resume to identify and recommend talent.

14. The method of claim 11, wherein the system further comprises
computing a candidate's fit as it pertains to a specified search criteria
utilizing interview assessment data, taking into account a volume of
historical assessment data available for each candidate and defined
weightage for at least one search term and candidate performance across
the at least one question relevant to the search term, and the complexity
of such question.

15. The method of claim 11, wherein the system further comprises
acquiring resumes using instructions that monitors arrival of new resumes
into a candidate information system resume repository by a multisource
talent acquisition system and processing the resumes.

16. A method for performing a multi-source talent acquisition, the method
comprising: computing a candidate's fit as it pertains to a search
criteria specified by a user utilizing test assessment data taking into
account a volume of historical assessment data available for each
candidate, user defined weightage for each search term and a performance
of a candidate across all questions relevant to a search term.

17. A method for performing a multi-source talent acquisition, the method
comprising: performing a contextual information search on the resumes;
evaluating a context of occurrence of each search term on the resumes in
order to efficiently value real-world project experience; efficiently
valuing at least one recent project experience; and identifying and
valuing possible certifications and specialist level skills.

18. The method of claim 17, further comprising constructing profile
images for at least one candidate using the at least one candidate's
resume and an XML record, where the profile image is a multidimensional
artifact encapsulating a holistic representation of the at least one
candidate's skills, experience and qualifications.

19. The method of claim 17, further comprising representing candidate
information in a multidimensional artifact where candidates with similar
profiles are clustered together in a multidimensional characteristic
space.

20. The method of claim 17, further comprising computing a recency factor
for each project on the candidate's XML record where there is an
occurrence of the search term.

21. The method of claim 17, further comprising identifying a number of
occurrences of star terms in proximity of the occurrences of each search
term in the candidate's resume, where star terms indicate a degree of
superiority of a skill used in the resume, and where proximity is defined
as a word distance range from the search term that the star terms are to
be looked and accounted for.

22. The method of claim 17, further comprising computing a candidate's
resume score for each search term based on a number of occurrences of the
search term, context of the occurrence, recency of use, number of
occurrences of star terms with proximity of the search term.

23. The method of claim 17 operating on a system, the system comprising:
a memory for storing instructions and data, the data comprising a set of
programs and a dataset having one or more data fields; and a server that
executes the instructions and processes the data.

24. The method of claim 23, wherein the system further comprises
constructing profile images for at least one candidate using the at least
one candidate's resume and an XML record, where the profile image is a
multidimensional artifact encapsulating a holistic representation of the
at least one candidate's skills, experience and qualifications.

25. The method of claim 23, wherein the system further comprises
representing candidate information in a multidimensional artifact where
candidates with similar profiles are clustered together in a
multidimensional characteristic space.

26. The method of claim 23, wherein the system further comprises
computing a candidate's resume score for each search term based on a
number of occurrences of the search term, context of the occurrence,
recency of use, number of occurrences of star terms with proximity of the
search term.

27. The method of claim 1 operating on an integrated platform executing
precision searches and viewing talent that has been identified, evaluated
and ranked based on information procured from multiple sources, the
platform comprising a multi-source talent acquisition system that
executes instructions and processes data.

28. The platform of claim 27, further comprising a user interface
communicating with at least the multi-source talent acquisition system
enabling specifying a search criteria, selecting a source, displaying
search results, displaying search summaries, displaying candidate
information and reports, displaying resume and profile images, and
providing a panel to select and specify candidates for further
assessment.

Description:

CLAIM FOR PRIORITY

[0001] This application is related to, and claims priority from, U.S.
Provisional Application No. 61/348,535 filed May 26, 2010 titled "Method
and System for Multi-Source Talent Information Acquisition, Evaluation,
and Cluster Representation of Candidates" the complete subject matter of
which is incorporated herein by reference in its entity.

FIELD OF THE INVENTION

[0002] The present invention relates generally to computing systems and
data processing. More specifically, it relates to a computer system and
method for acquiring information on prospective candidates from multiple
sources and evaluating their candidacy for job openings.

BACKGROUND OF THE INVENTION

[0003] The term Human Capital refers to the stock of talent and ability
embodied within the workforce population of an organization. More simply
stated, it refers to the people that make an organization. While
companies have always recognized the importance of human capital to their
economic growth, the accelerated shift to knowledge-based economy in
recent times has further accentuated its importance. Thus, the ability to
identify and hire the right talent in the shortest amount of time
possible coupled with the ability to retain such hired talent is vital to
an organization's ability to stay on top of the global economy. This has
direct bearing on the talent acquisition mechanisms available to
organizations today to achieve these goals.

[0004] Typically, when an organization needs to hire a new employee,
either on a permanent basis or contract basis, often times the hiring
manager in collaboration with the human resources manager, drafts a
position profile that describes the characteristics expected of the new
employee. The position profile typically consists of a detailed
description of the role, the skills, knowledge, experience and education
required to perform in the role, the team profile, cultural aspects,
duration of the position, and commercial aspects associated with the
position. This then is published to either an in-house corporate
recruitment team and/or a recruitment agency for fulfillment.

[0005] Traditionally, the group in-charge of fulfilling the job opening
advertises the position on print or electronic media and receives resumes
from prospective candidates in response to the advertisement. The resumes
are then manually reviewed to assess the qualifications of the candidate,
and those candidates whose resumes appear to reflect the qualifications
called for in the position are then invited for an interview. This
process has several drawbacks associated with it, some of which include
the limited reach of the job advertisement and manual review of the
resumes which is time consuming and error prone. This not only results in
qualified candidates either not applying for the position due to the poor
reach of the advertisements or not being invited for an interview due to
human error in the manual resume review process, but also resumes of less
qualified candidates being assessed incorrectly leading to loss of time
and possible mis-hire.

[0006] Prior art systems such as job boards address these inadequacies to
some extent by providing tools for candidates seeking new opportunities
to upload their resumes into their system. In addition to advertising the
job opening, recruiting agents are offered tools to perform searches for
prospective candidates from amongst those candidates that have posted
their resumes on the job board's system. This process requires for the
recruiting agent to specify to the system a set of keywords representing
the skills/qualifications expected of the candidate and then execute a
search. Often times, the prior art system executes a textual keyword
search through the body of text contained in the candidates' resumes, and
returns to the user those resumes that have occurrences of the keywords
specified by him.

[0007] One of the major drawbacks of this method is that the use of
keyword search to identify prospective candidates more often than not
results in a large number of resumes being returned to the user as
matches with only a fraction of these results being likely `true
matches`, the contributory reason being that a textual word match is all
that it takes for a resume to get qualified as a match. Often times, such
systems do not have the ability to discern the context in which such
keywords appear on the candidate's resume, thus likely returning a
candidate with five or more occurrences of a certain keyword under his
academic coursework section done over a decade ago above a candidate with
four occurrences of the same keyword in a description related to his work
on a current project. Thus, it is left to the user yet again to manually
review the large number of resumes returned by the system to weed out the
pseudo-matches and identify truly qualified candidates for further
assessment. This process has several problems associated with it. The
most obvious of the problems is the amount of time consumed in reviewing
the large number of results to identify the `true` matches. Even
comprehensive keywords specification most times result in matches
numbering in the thousands, with no means of identifying the
pseudo-matches from the true-matches without a manual visual review
through each of the resumes. In addition to being a daunting task, the
limited amount of time available to recruiting agents to fulfill
positions more often than not causes them to oversee qualified resumes
and in the process lose out on the talented candidates that they belong
to.

[0008] As a result, it would be desirable to provide a talent acquisition
system that is capable of analyzing resumes in a more human-like fashion,
particularly with the ability to understand the context of use of
keywords contained within the body of text contained in a candidate's
resume.

[0009] Another inherent problem presented by job board systems is the
tendency to favor `active` candidates over `passive` candidates while
presenting search results to the user. Active candidate refers to those
candidates that have engaged in recent activity on the job board system.
This could include uploading a resume, making changes to an existing
resume, applying for a position on the job board etc. The reasoning
behind favoring `active` candidates over `passive` candidates while
presenting them to the user is to increase the likelihood of availability
of the candidate picked by the user from amongst the large number of
search results returned to him. Assuming that the user is unlikely to
browse past the first fifty or so results out of the total thousand
presented to him, it makes intuitive sense for the system to position the
active candidates over the passive candidates while presenting them to
the user. While this appears as an elegant solution, the approach reveals
another critical setback. Often times, the most talented of candidates
are those that are already engaged on assignments and far less frequently
not on one. These are candidates that are seldom actively looking for
other engagements. In other words, these are passive candidates.
Considering the possibility that the candidate that the recruiting agent
is seeking belongs to the passive candidate pool, there is a fair amount
of chance that the candidate's profile never makes it to the purview of
the agent while executing a search using the approach indicated above.

[0010] While the value delivered by resumes in talent search cannot be
denied, excessive reliance on resumes alone as a source of information on
prospective talent by prior art systems has its pitfalls. This is
particularly more pronounced when it comes to using them to shortlist the
first set of candidates of interest. First, resumes are a candidate's
representation about himself. Since there is no central authority
reviewing and standardizing the representations made by candidates,
resume content is highly subjective in nature. A candidate, therefore,
whose resume takes a conservative approach to describing his experience,
would likely have a significantly different hit-rate compared to a
candidate with almost identical experience that takes a more superlative
approach to description of his capabilities on his resume. Second, in
addition to embellishments, falsification of facts on resume by
unscrupulous candidates is a known problem in the industry. While
background checks (employment and education verification) performed by
organizations serve to screen out such candidates, it must be remembered
that such screening typically happens much later in the hiring process.
By this time, genuinely qualified candidates whose resumes lost out in
the search results to falsified and embellished resumes, are likely no
longer available, not to mention of the loss of time and money for
organizations and recruitment agencies due to the prolonged search.
Third, due to limitations of space, resumes are unable to adequately
capture all of a candidate's experience and capabilities. At the best,
they serve to summarize his or her career in a manner that best appeals
to all of the targeted audience. This lends itself to the problem of a
resume likely not having sufficient occurrence of the specific keywords
used by a recruiting agent as part of his search criteria, and as a
result not getting showcased when search results are presented to the
user.

[0011] Thus, there are serious drawbacks to relying on resumes alone as
the only source of information on prospective talent in the first stage
of search process while attempting to identify and shortlist candidates
for further assessment, particularly using the keyword search approach
employed by prior electronic systems. There is a huge benefit to be
derived, both in terms of cost and time, if we have a mechanism that
enables us to identify truly qualified prospective resources right at the
first stage of the talent search process. More specifically, a mechanism
that is capable of accessing and analyzing objective and standardized
information on a candidate's capabilities, in addition to being able to
execute a contextual information search through resumes, in order to
identify and recommend talent.

[0012] An example of such objective and standardized information is
assessment data. Most hiring processes typically involve administration
of one or more forms of assessment, such as tests and interviews, to
candidates in order to assess the suitability of the candidate for the
targeted position. Often times, while the results of such assessments are
put to great use in determining the suitability of the candidate for that
specific position, no formal mechanisms exist to leverage the information
gathered over an extended period of time, as a result of many such
assessments that the candidate would have been administered, in analyzing
and recommending his or her suitability during first level searches
executed for other positions in the future. There is immense value in
such data, and it would be desirable to provide a system that is capable
of analyzing a candidate's performance across multiple past assessments
that have relevance to the skills and qualifications embodied in the
position profile that a search is currently being executed for, either in
whole or part.

[0013] Another drawback presented by prior art systems relates to the
method used to present matching candidates to the user. Often times,
candidates that are deemed to match the criteria specified by the user
are generally presented in a textual list format that typically spans
over multiple pages depending on the number of matching candidates. Given
the likelihood of the large number of candidates returned to the user as
a result of a search, this method of presentation makes it difficult to
not only ascertain the relevancy of one specific candidate to the search
in relation to other displayed candidates, but also ascertain the
similarities between displayed candidates.

SUMMARY OF THE INVENTION

[0014] Embodiments of the present invention relate to a computer system,
method and apparatus that serve to address the inadequacies of the prior
act systems described in the previous section. The system, method and
apparatus comprises a multi-source talent information acquisition system
that provides users engaged in the hiring/recruitment process an
integrated platform to execute precision searches and view talent that
has been identified, evaluated and ranked based on information procured
from multiple sources. The system, method and apparatus further comprises
performing contextual information search on candidate resumes, in order
to better assess the level of candidate's familiarity with the search
criteria, by evaluating the context of occurrence of each search term on
the candidate's resume. The system, method and apparatus further
comprises ability to integrate with assessment systems, access, retrieve
and analyze information relating to candidate performance in order to
evaluate candidature for the position, based on standardized and
objective information. The system, method and apparatus further comprises
a multidimensional profile imaging approach to representing candidate
information, where candidates with similar profiles are clustered
together in a multidimensional characteristics space. The system, method
and apparatus further comprises representation of candidates by means of
graphical objects such as spheres in a two dimensional space where
candidates with similar profiles are clustered together. The system,
method and interface further comprise ability to integrate with a
position profile registration system to access and retrieve search
criteria pertaining to a predefined position. The system, method and
apparatus further comprise ability to assign varying weightage to
components of the search criteria. The system, method and apparatus
further comprises utility to select and specify candidates for further
assessment. The system, method and apparatus further comprises a user
interface for search criteria specification, source selection, search
results display, search summary display, candidate information and
reports display, resume and profile image display, and a panel to select
and specify candidates for further assessment.

[0015] One embodiment of the present invention relates to a method for
performing a multi-source talent acquisition, the method including
entering search criteria; selecting at least one source from a plurality
of sources; executing a search using at least the search criteria and the
at least one source; identifying at least one talent match; and
displaying the at least one talent match.

[0016] One or more embodiments relate to entering the search criteria
including assigning varying weightage to components of the search
criteria; integrating with a position profile registration system to
access and retrieve the search criteria pertaining to a predefined
position; accessing and analyzing objective and standardizing information
on a candidate's capabilities and executing a contextual information
search through at least one resume to identify and recommend talent:
integrating with at least one assessment system, accessing, retrieving
and analyzing relating to a candidate's performance for evaluating
candidature for a position, based at least on standardized and objective
information; analyzing a candidate's performance across multiple past
assessments having relevance to skills and qualifications embodied in a
position profile for which at least a part of a search is being executed
for; searching and evaluating candidates based on information stored in
an interview system; computing a candidate's fit as it pertains to a
specified search criteria utilizing interview assessment data, taking
into account a volume of historical assessment data available for each
candidate and defined weightage for at least one search term and
candidate performance across the at least one question relevant to the
search term, and the complexity of the such question; acquiring resumes
using instructions that monitors arrival of new resumes into a candidate
information system resume repository by a multisource talent acquisition
system and processing the resumes; and/or representing candidates using
graphical objects in a two dimensional space, where candidates with
similar profiles are clustered together.

[0017] One or more embodiments relate to one or more methods operating on
a system for computing a total candidate test score for at least one
candidate utilizing parameters, the system including a memory for storing
instructions and data, the data include a set of programs and a dataset
having one or more data fields; and a server that executes the
instructions and processes the data. One or more embodiments of the
system may include integrating with a position profile registration
system to access and retrieve the search criteria pertaining to a
predefined position; accessing and analyzing objective and standardizing
information on a candidate's capabilities and executing a contextual
information search through at least one resume to identify and recommend
talent; computing a candidate's fit as it pertains to a specified search
criteria utilizing interview assessment data, taking into account a
volume of historical assessment data available for each candidate and
defined weightage for at least one search term and candidate performance
across the at least one question relevant to the search term, and the
complexity of such question; and/or acquiring resumes using instructions
that monitors arrival of new resumes into a candidate information system
resume repository by a multisource talent acquisition system and
processing the resumes.

[0018] Still another embodiment relates to a method for performing a
multi-source talent acquisition, the method including computing a
candidate's fit as it pertains to a search criteria specified by a user
utilizing test assessment data taking into account a volume of historical
assessment data available for each candidate, user defined weightage for
each search term and a performance of a candidate across all questions
relevant to a search term.

[0019] Still one or more embodiments relate to a method for performing a
multi-source talent acquisition, the method including performing a
contextual information search on the resumes; evaluating a context of
occurrence of each search term on the resumes in order to efficiently
value real-world project experience; efficiently valuing at least one
recent project experience; and identifying and valuing possible
certifications and specialist level skills.

[0020] One or more embodiments of the method include constructing profile
images for at least one candidate using the at least one candidate's
resume and an XML record, where the profile image is a multidimensional
artifact encapsulating a holistic representation of the at least one
candidate's skills, experience and qualifications; representing candidate
information in a multidimensional artifact where candidates with similar
profiles are clustered together in a multidimensional characteristic
space; computing a recency factor for each project on the candidate's XML
record where there is an occurrence of the search term; identifying a
number of occurrences of star terms in proximity of the occurrences of
each search term in the candidate's resume, where star terms indicate a
degree of superiority of a skill used in the resume, and where proximity
is defined as a word distance range from the search term that the star
terms are to be looked and accounted for; and/or computing a candidate's
resume score for each search term based on a number of occurrences of the
search term, context of the occurrence, recency of use, number of
occurrences of star terms with proximity of the search term.

[0021] Yet one or more embodiments relate to a method operating on a
system, the system including a memory for storing instructions and data,
the data including a set of programs and a dataset having one or more
data fields; and a server that executes the instructions and processes
the data; constructing profile images for at least one candidate using
the at least one candidate's resume and an XML record, where the profile
image is a multidimensional artifact encapsulating a holistic
representation of the at least one candidate's skills, experience and
qualifications; representing candidate information in a multidimensional
artifact where candidates with similar profiles are clustered together in
a multidimensional characteristic space; and/or computing a candidate's
resume score for each search term based on a number of occurrences of the
search term, context of the occurrence, recency of use, number of
occurrences of star terms with proximity of the search term.

[0022] Still another embodiment relates to one or more methods operating
on an integrated platform executing precision searches and viewing talent
that has been identified, evaluated and ranked based on information
procured from multiple sources, the platform including a multi-source
talent acquisition system that executes instructions and processes data.
In at least one embodiment a user interface communicates with at least
the multi-source talent acquisition system enabling specifying a search
criteria, selecting a source, displaying search results, displaying
search summaries, displaying candidate information and reports,
displaying resume and profile images, and providing a panel to select and
specify candidates for further assessment.

[0023] The foregoing and other features and advantages of the invention
will become further apparent from the following detailed description of
the presently preferred embodiment, read in conjunction with the
accompanying drawings. The drawings are not to scale. The detailed
description and drawings are merely illustrative of the invention rather
than limiting, the scope of the invention being defined by the appended
claims and equivalents thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG. 1 is a schematic representation of a system and method
according to the present invention;

[0025] FIG. 2 is an illustration of an exemplary hardware arrangement for
implementing the method and system of FIG. 1;

[0026] FIG. 3 is a schematic representation of a system and method
according to the present invention;

[0045] FIG. 20 is a flow chart of an embodiment of the method and system
of the present invention using resumes as a source;

[0046] FIG. 21a is an exemplary web page for the method and system of FIG.
1;

[0047] FIG. 21b is an exemplary web page for the method and system of FIG.
1;

[0048] FIG. 22 is an exemplary web page for the method and system of FIG.
1;

[0049]FIG. 23 is an exemplary web page for the method and system of FIG.
1;

[0050] FIG. 24 is an exemplary web page for the method and system of FIG.
1;

[0051] FIG. 25 is an exemplary web page for the method and system of FIG.
1;

[0052] Throughout the various figures, like reference numbers refer to
like elements.

DETAILED DESCRIPTION OF PRESENTLY PREFERRED EMBODIMENTS

[0053] In the description that follows, the subject matter of the method
and system will be described with reference to acts and symbolic
representations of operations that are performed by one or more
computers, unless indicated otherwise. As such, it will be understood
that such acts and operations, which are at times referred to as being
computer-executed, include the manipulation by the processing unit of the
computer of electrical signals representing data in a structured form.
This manipulation transforms the data or maintains it at locations in the
memory system of the computer which reconfigures or otherwise alters the
operation of the computer in a manner well understood by those skilled in
the art. The data structures where data is maintained are physical
locations of the memory that have particular properties defined by the
format of the data. However, although the subject matter of the
application is being described in the foregoing context, it is not meant
to be limiting as those skilled in the art will appreciate that some of
the acts and operations described hereinafter can also be implemented in
hardware, software, and/or firmware and/or some combination thereof.

[0054] FIG. 1 illustrates a high-level overview of the method and system
proposed in the present invention. The method and system of the present
invention can be accomplished using a variety of hardware arrangements.
FIG. 2 illustrates an exemplary hardware arrangement. The multi-source
talent acquisition system 100 is data connected with the position profile
registration system 104, candidate information system/resume repository
108, test system/test scores repository 112, and the interview
system/interview scores repository 114. Position profile registration
system refers to a method and system used by hiring managers and
recruiting agents to define and register details about a position that
they are seeking to fill by means of a position profile. In one
embodiment, the position profile consists of a position name, position
number, position type (contract, fulltime, etc.), location, duration,
detailed description of the role, the skills, knowledge, experience and
education required to perform in the role, the team profile, cultural
aspects, and commercial aspects associated with the position. In a
further embodiment, each of the skills within a position profile is
associated with a weight that is intended to indicate the importance of
that skill in relation to the rest of the skills defined within the
position profile. A test system refers to a method and system that
facilitates administering tests to candidates and recording the
performances of candidates in such tests. In one embodiment, the test
system is a web-based system that enables administration of tests over
the internet and for candidates to take up the test remotely from a
location of their choice. Candidate performance for each question of the
test is monitored, captured and stored in a repository by the test
system. In one embodiment, the interview system is a method and system
that enables scheduling and administration of interviews to candidates,
and recording of scores that indicate candidate performances in such
interviews.

[0055] An embodiment of the multi-source talent acquisition system is
composed of a web server 208 and a database server 210, which communicate
with the network 200 through a firewall 206. The web server 208 and
database server 210 include a computer with a display, input/output
devices, processor, memory and storage device. The computer uses any one
of the commercially available operating systems such as Windows Server
2003, and runs a commercially available web server application such as
Internet Information Services. The database server 210 includes any
relational database such as SQL Server. The software programs that
represent the disclosed methods reside in the storage device, and are
executed by the processor.

[0056] The position profile registration system 104, candidate information
system/resume repository 108, test system/test score repository 112, and
interview system/interview score repository 114 are each composed of a
web server (214, 220, 226, 232) and database server (216, 222, 228, 234)
that include a computer with a display, input/output devices, processor,
memory and storage device and communicate with the network 200 through a
firewall (212, 218, 224, 230). In one embodiment, one or more of the
systems listed above share a common web server and data server. In an
alternate embodiment, the systems are housed in separate web servers and
data servers and communicate with each other through the network 200.

[0057] In one embodiment, user 102a communicates with the multi-source
talent acquisition system 100 through the network 200 by operating a
computer 202b. The computer 202b is a personal computer or a laptop that
includes a display, input/output devices, processor, memory and data
storage, and runs any of the commercially available operating systems
such as Windows XP, Windows Vista etc. In another embodiment, user 102a
communicates with the multi-source talent acquisition system 100 through
the network 200 by operating a handheld device 202a such as a cell phone.
The handheld device 202a and computer 202b invoke browsers 204a and 204b
respectively for the user 102a to communicate with the multi-source
talent acquisition system 100. Examples of browser 204a and 204b include
Internet Explorer, Mozilla Firefox, and Safari.

[0058] The hardware components shown in FIG. 2 and those described above
are intended to be illustrative of the components that they represent and
are therefore exemplary in nature and not intended to limit the scope of
the present invention.

[0059] FIG. 3 illustrates a detailed view of the components included
within the multi-source talent acquisition system 100. User interface 106
refers to the set of components displayed on the web page pertaining to
the multi-source talent acquisition system 100 and is accessed by the
user 102a on browser 204a. The components of the user interface 106 are
represented by means of graphical elements on the web page and enable the
user to interact with the software programs contained within the
multi-source talent acquisition system 100. The programs contained within
the multi-source talent acquisition system and the user interface can be
implemented using a number of tools and languages suited for the purpose,
some of which include .NET, Silverlight, Flex, etc. The components of the
user interface 106 include position profile access control 302, search
criteria entry and weight specification field 304, source selection
utility 306, search results display 310, search results zoom/pan control
300, search summary display 308, candidate profile display 312, candidate
synopsis/skills display 314, candidate score/report display 316, and
administration control 318. The multi-source talent acquisition system
100 further includes a resume processing unit 116 that serves to access
the candidate information system/resume repository 108 and process the
retrieved resumes. The resume processing unit 116 further includes
software programs such as document convertor 338, parser 340, profile
image builder 342, and cluster constructor 344. The multi-source talent
acquisition system 100 further includes an assessment scores processing
unit 346 that serves to access the test system/test score repository 112
and the interview system/interview score repository 114, and process the
retrieved information. The assessment scores processing unit 346 further
includes software programs such as test score computation 346 and
interview score computation 348. Information processed by the programs
contained within the resume processing unit 116 and assessment scores
processing unit 118 are stored in the database 120, also contained within
the multi-source talent acquisition unit 100. Other programs contained
within the multi-source talent acquisition system 100 include search
engine 122, evaluation and ranking engine 124, WTQC (weighted total
question count) threshold control 320, candidate manager & report
generator 322, and admin manager 324.

[0060] The description above only serves to illustrate the components
contained within an embodiment of the multi-source talent acquisition
system 100. The methods represented by these components and their
purposes will be more readily understood upon consideration of the
attached diagrams and the rest of the detailed description contained
within this document.

[0061] Method Overview

[0062] This section details an overview of the workings of the method and
system proposed in the present invention. Subsequent sections will
present embodiments of the method in finer detail. For purposes of
illustration, search terms and skills pertaining to the field of
Information Technology have been used. As those skilled in the art will
understand, the method and system proposed in the present invention can
be applied to a wide range of fields.

[0063] In FIG. 4, a flowchart representing the overview of the method is
presented. FIG. 6 illustrates an exemplary screenshot of the webpage
representing the multi-source talent acquisition system 100, as viewed by
a user, after a search is executed. In one embodiment, referring to FIG.
4, in step 402, the user first accesses the multi-source talent
acquisition system 100 by entering the uniform resource locator (URL)
corresponding to the web server 208 hosting the multi-source talent
acquisition system 100, in the browser.

[0064] FIG. 5 illustrates an exemplary screenshot of the login webpage
that is first presented to the user in his browser in response to his
attempt to access the multi-source talent acquisition system 100. The
user enters his username and password in the fields 502 and 504
respectively, and clicks on the login button 506. Referring to FIG. 4, in
step 404, the login information is transmitted back to the multi-source
talent acquisition system 100 through the network 200 for authentication.
Once the user's login credentials have been authenticated, the user is
presented with a webpage that represents the multi-source talent
acquisition system's screen. The webpage is as illustrated in FIG. 6, but
is devoid of any information related to the search criteria, search
results or candidate.

[0065] Referring to FIG. 4, in step 406, the user enters the search
criteria in field 602 of the webpage 600 as illustrated in FIG. 6. In one
embodiment, the user enters the terms and associated weights representing
the search criteria directly into the field 602. In another embodiment,
the user loads the search terms from an existing position profile. The
user does so by clicking on the search glass icon 604, and then
performing a search for the specific position. In the latter case, the
multi-source talent acquisition system connects with the position profile
registration system 104, and retrieves information in regards to the
desired position in order to display it on the webpage 600. In the
example shown in FIG. 6, the user has entered the search criteria `Java
[40], j2ee [30], oracle [30]`, where `Java`, `j2ee`, and `oracle` are the
skills sought, and the weightage assigned by the user for each of the
terms are `40/100`, `30/100`, and `30/100`.

[0066] Referring to FIG. 4, in step 410, the user selects the source using
the dropdown list 606. The dropdown list consists of the list of sources
of information such as Resumes, Test System, and Interview System that
the multi-source talent acquisition system has access to and that the
user can base the search on. In one embodiment, the user selects one
source from the list and initiates the search by clicking on the search
button 608. This will execute a search based on the information present
in that source. In an alternate embodiment, the user may select multiple
sources in order for the system to execute a search based on the
information contained within all of the selected sources at the same
time.

[0067] Referring to FIG. 4, in step 412, the multi-source talent
acquisition system 100 accesses the system corresponding to the source(s)
selected by the user, identifies matches, ranks and displays results in
the search results display panel 610. Each candidate that is part of the
search result is represented on the search results display panel 610 by
means of a candidate object 612a. In one embodiment, spheres labeled with
the names of candidates are used as candidate objects. In alternate
embodiments, any graphical shape/element may be used as candidate
objects. In addition, per step 416 illustrated in FIG. 4, the web page
600 also displays a summary of the search results in the search summary
display panel 616. The information displayed in the search summary
display panel 616 includes `number of candidates searched`, `number of
candidates that match the search criteria from amongst those searched`,
`number of sources searched`, and a graphical chart to represent the
number of matching candidates for each component of the search criteria.

[0069] Returning to FIG. 4, in step 416, when the user clicks on a
candidate object 612a, information pertaining to the candidate
represented by the candidate object 612a gets displayed on the candidate
profile display panel 618, candidate synopsis/skills display panel 620,
and the candidate score/report display panel 622. The candidate profile
display panel 618 includes information such as candidate's name,
location, contact details, video profile, availability status, and links
to external websites that carry more information about the candidate. The
synopsis/skills display panel 620 includes a skills matrix and a
professional summary about the candidate, as well as links/icons to
display the candidate's resume and profile image. The score/report
display panel 622 includes graphical charts that represent a summary of
the candidate's skills as it pertains to the search criteria, and a
button/link to open a more detailed report of the candidate's standing as
it pertains to the search criteria.

[0070] Referring to FIG. 4, in step 420, the user may now perform a wide
variety of actions pertaining to the search. This includes and is not
limited to viewing the candidate's video profile and accessing the
candidate's external web pages from the candidate profile display panel
618, reviewing the candidate's skills, resume and profile image in the
candidate synopsis/skills display panel 620, pulling up and reviewing a
detailed report of the candidate's skills as it pertains to the search
criteria in the score/report display panel 622. In addition, the user may
also shortlist a candidate for further assessment, by selecting a
candidate object 612a representing a candidate, and clicking on the
schedule test button located in the shortlisted candidates panel 624. In
another embodiment, the user may also add candidates to the list in the
shortlisted candidates panel 624 by clicking on the `add to schedule
test` button in the profile snapshot window 614 that pops up while
placing the mouse pointer over a candidate object.

[0071] Having reviewed the candidates presented on the search results
display panel 610, the user may now choose to view more candidates for
the existing search criteria (step 422 illustrated in FIG. 4) or execute
a new search by specifying a new search criteria (step 424 illustrated in
FIG. 4). In the case of the former, in step 426 illustrated in FIG. 4,
the user zooms-out or pans using the zoom/pan control 626 to enable a
higher level view of the search results display panel 610. This will
result in more candidate objects coming into view on the search results
display panel 610. The user may use the zoom/pan control 626 any number
of times after a search is executed in order to control the number of
candidate objects being displayed on the search results display panel
610. Should the user decide to start a new search, the user will return
to step 406 illustrated in FIG. 4, and enter new search criteria in the
search criteria entry field 602.

[0072] The rest of the document serves to describe each part of the method
and system in finer detail.

[0073] Search Criteria Entry Phase

[0074] This is the first phase of the method, after a user has logged in
to the multi-source talent acquisition system 100. The user specifies the
search criteria as a set of search terms and weights associated with each
search term. Weights specification enables the user to prioritize one
skill over another while identifying talent. FIG. 7 shows a closer view
of the section of the web page 600 that relates to search criteria entry.
As indicated in the overview section, the system provides users with two
methods of search criteria entry. In one embodiment, the user enters the
terms and associated weights representing the search criteria directly
into the field 602. In another embodiment, the user loads the search
terms from an existing position profile.

[0075] FIG. 10 is a flowchart that illustrates the method of the first
embodiment, where the user directly enters the terms representing the
search criteria. FIG. 8 illustrates various scenarios encountered in this
method, and will be referenced to in the description that follows.

[0076] Referring to step 1004 of FIG. 10 and diagram 802 of FIG. 8, when a
user regards all the search terms as being equally important, the user
enters the terms delimited by commas into the search criteria entry field
602. In the example shown in diagram 802 of FIG. 8, the user has
specified search terms `Java, j2ee, spring, hibernate, agile`. The
multi-source talent acquisition system 100 would assume equal weightage
for all terms while identifying and assessing prospective candidates.
Also as it can be seen in diagram 802 of FIG. 8, the `assigned weight
balance` reads as 100, implying that no specific weight has been assigned
to any of the search terms by the user.

[0077] Referring to steps 1006 and 1008 of FIG. 10, a user may choose to
assign varying weightage to the search terms. The user does this by
including the weight within `square brackets` immediately following each
search term in the search criteria entry field 602. The `assigned weight
balance` gets adjusted automatically to indicate the balance of weight
points that are left to be assigned. Referring to FIG. 10, in step 1010,
when the total of the weights assigned by the user to the search terms
exceeds 100, the system will alert the user of the error, and request him
to amend the weights allocation. Referring to step 1012 of FIG. 10 and
diagram 804 of FIG. 8, when the user specifies weights for only some of
the search terms before executing the search, the system will
automatically allocate the balance of the unallocated weight points
equally amongst the rest of the terms while identifying and assessing
prospective candidates. Referring to the example shown in diagram 804 of
FIG. 8, the user has chosen to indicate that the search term `Java`
occupies a weightage of 40 points out of a total of 100. The `assigned
weight balance` gets adjusted automatically to indicate the balance of
weight points that can be user-assigned. Since the user has specified
weight points for the term `Java` alone, should the user now execute a
search without indicating specific weight points for the rest of the
terms, the system will automatically distribute the balance of the
unallocated weight points equally amongst the rest of the terms. This
will result in the following weight distribution: Java-40; J2EE-15;
Spring-15; Hibernate-15; Agile-15. When the user clicks on the search
button 608 after selecting a source from the dropdown list 606, the
display in the search criteria entry field 602 will be updated by the
system to reflect the weights as allocated by it, as shown in diagram 806
of FIG. 8.

[0078] Referring to step 1014 of FIG. 10 and diagram 808 of FIG. 8, when
the user allots all of the available 100 weight points amongst only some
of the search terms he specified in the search criteria entry field 602,
it results in zero weight points left to be assigned and will cause the
rest of the search terms to be grayed out, implying that they will not be
included as part of the search criteria. However, the user may choose to
modify this, and re-allot weights prior to executing the search, or in a
subsequent search run. Referring to the example illustrated in diagram
808 of FIG. 8, the user has allotted all of the 100 weights points
between only two of the search terms. This causes the rest of the search
terms that have no weights left to be assigned to be grayed out, and not
included as part of the search criteria.

[0079] FIG. 11 is a flowchart that illustrates the method of the second
embodiment, where the user loads the search terms from an existing
position profile. FIG. 9 is a screenshot of the position search window
that plays a role in this method. Referring to steps 1102 and 1104 of
FIG. 11, the user clicks on the position search button represented by the
search glass icon 604, causing the position search pop-up window 900 to
open up. Referring to step 1106 in FIG. 11, the user executes a search
for predefined positions by entering information in one or more of the
fields contained in the position search pop-up window 900. These fields
include position number 902, client name 904, position name 906, and
position registration date 908. Referring to FIG. 11, in step 1108, when
the user clicks on the search button 910, the multi-source talent
acquisition system 100 accesses records in the position profile
registration system 104, searches for positions that match the criteria
specified by the user, and displays matching records in table 912 of the
position search pop-up window 900. Referring to steps 1110 and 1112 off
FIG. 11, when the user then reviews the results displayed in the table
912, and clicks on the listing corresponding to the position of interest,
the search terms and weights predefined in the position profile
corresponding to the selected position gets loaded in the search criteria
entry field 602. The user thereafter reviews the search criteria and
makes changes as required in the search criteria entry field 602 before
selecting a source and executing a search by clicking on the search
button 608.

[0080] Source Selection Phase

[0081] An embodiment of the multi-source talent acquisition system enables
users to select and specify the sources of information that is to be used
in identifying and evaluating prospective talent. In one embodiment, the
choices of sources are presented to the user by means of a dropdown list
606 on the web page pertaining to the multi-source talent acquisition
system. The choices can include sources such as resumes, test data, and
interview data. The user selects one source from the list and initiates
the search by clicking on the search button 608. This will execute a
search based on the information present in that source. In an alternate
embodiment, the choices of sources are presented to the user by means of
a multiple-selection list enabling the user to select multiple sources in
order for the system to execute a search based on the information
contained within all of the selected sources at the same time. When the
user specifies the source(s) and clicks the search button 608, the
multi-source talent acquisition system will access the systems
representing the specified sources, in order to search and evaluate
information pertaining to prospective candidates based on the search
criteria specified by the user. The next few sections will elaborate the
method as it pertains to each of the sources.

[0082] Test Data as Source

[0083] Most hiring processes typically involve administration of one or
more tests to candidates in order to assess the suitability of the
candidate for the targeted position. The large majority of such tests are
typically administered over the web, enabling candidates to take the
tests remotely. In an alternate scenario, test systems that permit
candidates to take up tests proactively for the purposes of
self-evaluation and certification also exist. In one embodiment, the test
system is integrated within the same platform as that of the multi-source
talent acquisition system 100. In an alternate embodiment, the test
system is external and communicates with the multi-source talent
acquisition system 100 over a data network 200. Questions administered as
part of such tests are characterized by the category and subject that it
belongs to, a set of keywords known as tags that best describe the
question, and complexity. When tests are administered, candidate
performances for each question administered as part of that test are
captured and stored in a repository. The candidate performance for each
question is characterized by whether the candidate answered the question
correctly, and the amount of time taken by the candidate to answer the
question. Over a period of time, the amount of information captured in
regards to a candidate's competencies in various skills as ascertained by
his performance across multiple tests that have been administered to his
in the past, can be of significant value in evaluating his suitability
for the position under consideration currently.

[0084] FIG. 12 is a flowchart that illustrates an overview of the method
as it applies to searching for and evaluating candidates based on
information stored in the test system 112. FIG. 13 is a flowchart that
illustrates the method of computing the candidate's score as it pertains
to the search criteria specified by the user. The process will encompass
steps that will account for the volume of historical assessment data that
is available for each candidate, user defined weightage for each search
term (in picking the initial set of candidates, and in computing the
final score), and the performance of the candidate across all questions
relevant to a search term (including ones that the candidate failed to
answer correctly).

[0085] Referring to FIG. 12, in step 1202, when the user enters the search
criteria and clicks on the search button, the multi-source talent
acquisition system 100 accesses the repository of questions in the
database server 228 of the test system 112, and identifies questions that
are relevant to the search criteria entered by the user in the search
criteria entry field 602. In one embodiment, the system does this by
searching through the content of the question and answer and the tags
associated with each question to identify occurrences of each term
contained within the search criteria. Questions that contain at least one
search term are considered a match. Referring to FIG. 12, in step 1204,
the system filters the set of identified questions in order to retain
only those that have one or more attempts registered. In step 1206
illustrated in FIG. 12, candidates that have correctly answered at least
one question corresponding to each of the search terms are identified. In
step 1208, illustrated in FIG. 12, sets are constructed for each search
term, with each set being composed of candidates that have answered at
least one question relevant to the search term correctly. In step 1210,
illustrated in FIG. 12, the system identifies and retains the sub-set of
candidates that occupy the intersection of all sets corresponding to each
of the search terms. This results in deriving the set of candidates that
have correctly answered at least one question relevant to each of the
search terms.

[0086] Referring to FIG. 14, if S1 1402, S2 1404, and S3 1406 represent
sets composed of candidates that have correctly answered at least one
question relevant to that search term, C 1408 refers to the sub-set of
candidates sought in step 1210 illustrated in FIG. 12. [0087] S1={set
of candidates that have answered at least one question bearing search
term T1, correctly} [0088] S2={set of candidates that have answered at
least one question bearing search term T2, correctly} [0089] . [0090] .
[0091] . [0092] Sn={set of candidates that have answered at least one
question bearing search term Tn, correctly}

[0092] C=S1∩S2∩ . . . ∩Sn

[0093] Returning to FIG. 12, in step 1212, the weighted total question
count (WTQC score) is computed for each of the candidates identified in
step 1210, illustrated in FIG. 12, as follows:

WTQC = n × i = 1 n [ QC i × ( w i 100 )
] ##EQU00001##

[0094] where WTQC is the `Weighted Total Question Count` for the
candidate, n is the number of search terms specified by the user, QC
(Question Count) is the number of questions identified as being answered
correctly by a candidate for a specific search term, and w is the user
specified weightage for the specific search term.

[0095] Returning to FIG. 12, in steps 1214 and 1216, the sub-set of
candidates C is sorted based on the weighted total question count (WTQC)
in the order of highest to lowest, and the top `n` candidates are
selected from the sorted list. In one embodiment, `n` is set based on the
number of candidates to be displayed by default on the search results
display 610. If the number of candidate objects to be made viewable by
default on the search results display 610 when the results are first
displayed after a search is completed is twenty, then `n` is set as 20.
The number of candidate objects displayed on the search results display
can thereafter be tweaked by using the zoom/pan control 626 as will be
detailed further on in the description. In an alternate embodiment, the
user will have a slider made available to them on the web page 600, that
they can use to select the WTQC threshold (minimum WTQC permissible) in
order to control the number of candidates picked for score computation
and eventually displayed. For instance, let us assume that the values of
WTQC computed for candidates in subset C range from 10 to 100, and that
the value of `n` is set as 20 in the admin screen. Assuming that the
candidate with the 20th highest WTQC score has a WTQC score of 60, the
slider control's default position on the user's screen will be at 60 and
the max and min values of the slider will be set at 100 and 10
respectively. Once the scores computation are completed, should the user
now wish to include more prospective candidates in the mix, the user may
move the slider control towards the `min value` so that candidates with
WTQC scores lower than 60 (corresponding to the 20th candidate) too are
included for further score computation. Alternatively, should the user
wish to further filter the number of prospective candidates based on
WTQC, the user will move the slider control towards the `max value`.

[0096] Returning to FIG. 12, in step 1218, the total candidate test score
is computed for each of the `n` candidates with the highest WTQC scores.
The flowchart in FIG. 13 illustrates the method of computing the total
candidate test score for each candidate. Referring to FIG. 13, in step
1302, for each question to be included in computation of the candidate's
total candidate test score, the following parameters are retrieved from
the test system 112.

[0097] Candidate-Specific Question Parameters

[0098] a. Whether the question was answered correctly by the candidate

[0099] b. Time taken by the candidate to answer the question (xi)

[0100] General Question Parameters

[0101] a. Total number of candidates that have been administered the
question (n)

[0102] b. Time taken by each candidate to answer the question

[0103] c. Maximum time taken by candidates to answer the question (M)

[0104] d. Complexity of the question (CF)

[0105] In step 1304, illustrated in FIG. 13, the average of the time taken
to answer each question by all candidates that have been administered the
question is calculated as

μ = 1 n × i = 1 n x i ##EQU00002##

[0106] In step 1306, illustrated in FIG. 13, standard deviation of `time`
distribution for each question (where `time` is time taken by all
candidates that were administered the question) is computed as

σ = i = 1 n ( x i - μ ) 2 ( n - 1 )
##EQU00003##

[0107] In step 1308, illustrated in FIG. 13, the candidate question score,
which indicates the candidate's performance in each question, is computed
as:

S i = { [ M - X i ] + δ σ } × CF
##EQU00004##

[0108] where M is the maximum time taken by candidates to answer the
question, Xi is the time taken by the candidate to answer the question,
δ is a small user defined offset value, and CF is the complexity
factor of the question. Complexity factor refers to a numerical value
that is representative of the complexity of a question. The following
table is an example of complexity factors for a test system that
categorizes questions into three levels of complexities.

[0109] As it can be seen, part of the formula used to compute the
candidate's performance score involves statistical normalization of data.
This is required, since the time-data for different questions could
potentially be spread across different ranges. Typical statistical
normalization involves conversion into normal distribution with a zero
mean and a variance of one. However, since this would result in negative
values for data points (which would be cumbersome for scoring), the
formula above provides a normalization mechanism that drives the data
point with the maximum time-data value towards a score of `almost` zero,
while ensuring that all points are assigned positive scores. While it
might seem logical to simply assign a score of zero to the data point
with the maximum value (M), it results in loss of ability to
differentiate between a candidate that took the longest to answer a
question with complexity S (simple), and one that took the longest to
answer a question with complexity C (complex), since the complexity
factor will cease to have any effect, when the preceding sub-formula
results in a value of zero. This is addressed by the introduction of
`δ` in the formula above. δ will help provide a small
user-defined offset in the scores, and will ensure that the complexity
factor retains effect. In one embodiment, δ is defined as:

δ=0.1×σ

[0110] In alternate embodiments, δ will be a user configurable value
that can be set using the administration control 318.

[0111] Returning to FIG. 13, in step 1310, the candidate search term score
for each search term, is computed by calculating the average of the
candidate question score across all identified questions pertaining to
the search term. In step 1312, illustrated in FIG. 13, the candidate
performance score for search term is derived by computing the product of
the candidate search term score and the ratio of `number of questions
pertaining to search term answered correctly to total number of questions
pertaining to search term administered`. In step 1314, illustrated in
FIG. 13, the candidate weighted performance score for search term is
derived by computing the product of candidate performance score and
weight percentage assigned by the user to the search term in the search
criteria entry field 602. In step 1316, illustrated in FIG. 13, the total
candidate test score is derived by computing the sum of candidate
weighted performance score across all search terms specified by the user.
The table below illustrates a snapshot of this process

[0112] Returning to FIG. 12, in step 1220, the candidates for whom the
total candidate test scores are computed are sorted based on the score in
the order of highest to lowest. In step 1222, the candidates are
displayed on the search results display panel 610, with each candidate
being represented by a candidate object and distributed on the panel on
the basis of their score, starting from the center of the search results
display panel 610 and leading towards the periphery. After having
reviewed the candidates displayed on the search results display panel
610, referring to step 1224, illustrated in FIG. 12, the user may now use
the zoom/pan control 626 to enable viewing of more candidates on the
screen. Zooming-out using the zoom/pan control 626 causes the WTQC
threshold value to be lowered, which in turn increases the number of
candidates that may be picked from the WTQC sorted list to have their
total candidate test scores computed. This results in more candidates
being available to be displayed on the search results display panel 610.

[0113] Interview Data as Source

[0114] Most hiring processes typically involve administration of one or
more interviews to candidates in order to assess the suitability of the
candidate for the targeted position. Certain interview systems support
recording of the candidate's performance scores by the assessor at the
completion of the interview. In one embodiment, the interview system is
integrated within the same platform as that of the multi-source talent
acquisition system 100. In an alternate embodiment, the interview system
is external and communicates with the multi-source talent acquisition
system 100 over a data network 200. Questions administered to candidates
by assessor as part of such interviews are characterized by the category
and subject that it belongs to, a set of keywords known as tags that best
describe the question, and complexity. When interviews are administered,
candidate performances for each question administered as part of the
interview are captured and stored in a repository. The candidate
performance for each question is typically characterized by a numerical
value assigned by the assessor to indicate his evaluation of the
candidate's response to the administered question. Over a period of time,
the amount of information captured in regards to a candidate's
competencies in various skills as ascertained by his performance across
multiple interviews that have been administered to him in the past, can
be of significant value in evaluating his suitability for the position
under consideration currently.

[0115] FIG. 15 is a flowchart that illustrates an overview of the method
as it applies to searching for and evaluating candidates based on
information stored in the interview system 114. FIG. 16 is a flowchart
that illustrates the method of computing the candidate's score as it
pertains to the search criteria specified by the user. The process will
encompass steps that will account for the volume of historical assessment
data that is available for each candidate, and user defined weightage for
each search term (in picking the initial set of candidates, and in
computing the final score).

[0116] Referring to FIG. 15, in step 1502, when the user enters the search
criteria and clicks on the search button, the multi-source talent
acquisition system 100 accesses the repository of questions in the
database server 234 of the interview system 114, and identifies questions
that are relevant to the search criteria entered by the user in the
search criteria entry field 602. In one embodiment, the system does this
by searching through the content of the question and answer and the tags
associated with each question to identify occurrences of each term
contained within the search criteria. Questions that contain at least one
search term are considered a match. Referring to FIG. 15, in step 1504,
the system filters the set of identified questions in order to retain
only those that have been administered at least once. In step 1506,
illustrated in FIG. 15, candidates that have been administered at least
one question corresponding to each of the search terms are identified. In
step 1508, illustrated in FIG. 15, sets are constructed for each search
term, with each set being composed of candidates that have been
administered at least one question relevant to the search term correctly.
In step 1510, illustrated in FIG. 15, the system identifies and retains
the sub-set of candidates that occupy the intersection of all sets
corresponding to each of the search terms. This step results in deriving
the set of candidates that have been administered at least one question
relevant to each of the search terms.

[0117] Referring to FIG. 14, if S1 1402, S2 1404, and S3 1406 represent
sets composed of candidates that have been administered at least one
question relevant to that search term, C 1408 refers to the sub-set of
candidates sought in step 1510, illustrated in FIG. 15.

[0118] S1={set of candidates that have answered at least one question
bearing search term T1, correctly}

[0119] S2={set of candidates that have answered at least one question
bearing search term T2, correctly}

[0120] Sn={set of candidates that have answered at least one question
bearing search term Tn, correctly}

C=S1∩S2∩ . . . ∩Sn

[0121] Returning to FIG. 15, in step 1512, the weighted total question
count (WTQC score) is computed for each of the candidates identified in
step 1510, illustrated in FIG. 15, as follows:

WTQC = n × i = 1 n [ QC i × ( w i 100 )
] ##EQU00005##

[0122] where WTQC is the `Weighted Total Question Count` for the
candidate, n is the number of search terms specified by the user, QC
(Question Count) is the number of questions identified as being
administered to a candidate for a specific search term, and w is the user
specified weightage for the specific search term.

[0123] Returning to FIG. 15, in steps 1514 and 1516, the sub-set of
candidates C is sorted based on the weighted total question count (WTQC)
in the order of highest to lowest, and the top `n` candidates are
selected from the sorted list. In one embodiment, `n` is set based on the
number of candidates to be displayed by default on the search results
display 610. If the number of candidate objects to be made viewable by
default on the search results display 610 when the results are first
displayed after a search is completed is twenty, then `n` is set as 20.
The number of candidate objects displayed on the search results display
can thereafter be tweaked by using the zoom/pan control 626 as will be
detailed further on in the description. In an alternate embodiment, the
user will have a slider made available to them on the web page 600, that
they can use to select the WTQC threshold (minimum WTQC permissible) in
order to control the number of candidates picked for score computation
and eventually displayed. For instance, let us assume that the values of
WTQC computed for candidates in subset C range from 10 to 100, and that
the value of `n` is set as 20 in the admin screen. Assuming that the
candidate with the 20th highest WTQC score has a WTQC score of 60, the
slider control's default position on the user's screen will be at 60 and
the max and min values of the slider will be set at 100 and 10
respectively. Once the scores computation are completed, should the user
now wish to include more prospective candidates in the mix, the user may
move the slider control towards the `min value` so that candidates with
WTQC scores lower than 60 (corresponding to the 20th candidate) too are
included for further score computation. Alternatively, should the user
wish to further filter the number of prospective candidates based on
WTQC, the user will move the slider control towards the `max value`.

[0124] Returning to FIG. 15, in step 1518, the total candidate interview
score is computed for each of the `n` candidates with the highest WTQC
scores. The flowchart in FIG. 16 illustrates the method of computing the
total candidate interview score for each candidate. Referring to FIG. 16,
in step 1602, for each question to be included in computation of the
candidate's total candidate interview score, the following parameters are
retrieved from the interview system 114

[0125] a. Performance score assigned by assessor to candidate for the
question (S)

[0126] b. Complexity of the question (CF)

[0127] In step 1504, illustrated in FIG. 15, the candidate performance
score across all questions for each search term is computed as:

CPS = i = 1 n [ S i × ( CF i i = 1 n CF i
) ] ##EQU00006##

where CPS is the candidate performance score for each search term, n is
the number of questions pertaining to the search term administered to the
candidate, S is the candidate score for a specific question, and CF is
the complexity factor of a question. Complexity factor refers to a
numerical value that is representative of the complexity of a question.
The following table is an example of complexity factors for an interview
system that categorizes questions into three levels of complexities.

[0128] In step 1606, illustrated in FIG. 16, the candidate weighted
performance score for search term is derived by computing the product of
candidate performance score and weight percentage assigned by the user to
the search term in the search criteria entry field 602. In step 1608,
illustrated in FIG. 16, the total candidate interview score is derived by
computing the sum of candidate weighted performance score across all
search terms specified by the user. The table below illustrates a
snapshot of this process

[0129] Returning to FIG. 15, in step 1520, the candidates for whom the
total candidate interview scores are computed are sorted based on the
score in the order of highest to lowest. In step 1522, illustrated in
FIG. 15, the candidates are displayed on the search results display panel
610, with each candidate being represented by a candidate object and
distributed on the panel on the basis of their score, starting from the
center of the search results display panel 610 and leading towards the
periphery.

[0130] After having reviewed the candidates displayed on the search
results display panel 610, referring to step 1524 illustrated in FIG. 15,
the user may now use the zoom/pan control 626 to enable viewing of more
candidates on the screen. Zooming-out using the zoom/pan control 626
causes the WTQC threshold value to be lowered, which in turn increases
the number of candidates that may be picked from the WTQC sorted list to
have their total candidate test scores computed. This results in more
candidates becoming available to be displayed on the search results
display panel 610.

[0131] Resumes as Source

[0132] An embodiment of the multi-source talent acquisition system enables
contextual information search on candidate resumes, in order to better
assess the level of candidate's familiarity with the search criteria, by
evaluating the context of occurrence of each search term on the
candidate's resume. Through use of the contextual search approach, the
multi-source talent acquisition system will be able to efficiently value
real-world project experience, efficiently value recent project
experience(s), and identify and value possible certifications and
specialist level skills

[0133] In one embodiment, resumes are acquired by recruiters from
candidates and are uploaded into a candidate information system/resume
repository 108. In an alternate embodiment, resumes are uploaded directly
into the candidate information system/resume repository 108 by
candidates. In addition to the resumes, the candidate information system
may also store other information related to the candidate including but
not limited to the candidate's current location and address, contact
details, photo and/or video profile, current availability, details of
work currently engaged in, and uniform record locators to web pages that
carry information about the candidate.

[0134] In one embodiment, the candidate information system/resume
repository is integrated within the same platform as that of the
multi-source talent acquisition system 100. In an alternate embodiment,
the candidate information system/resume repository is external and
communicates with the multi-source talent acquisition system 100 over
data network 200.

[0135] FIG. 17 is a flowchart that illustrates the method of acquisition
of a resume by the multi-source talent acquisition system and the
subsequent processing of it. In step 1702, illustrated in FIG. 17, a
trigger service creates an alert whenever a new resume gets uploaded into
the candidate information system/resume repository 108. The trigger
service is a software program that keeps monitoring the arrival of new
resume files into the candidate information system/resume repository 108,
and generates a signal/message on realization of a new resume file being
uploaded.

[0136] In step 1704, illustrated in FIG. 17, following the alert by the
trigger service, a copy of the newly uploaded resume is transferred from
the candidate information system/resume repository 108 to the
multi-source talent acquisition system 100 using the file transfer
protocol over the network 200. In one embodiment, this transfer happens
immediately on receipt of the alert about the new resume file. In an
alternate embodiment, a batch processing software program runs during
prespecified intervals, such as once a day, and transfers files that have
been uploaded since the last transfer.

[0137] In step 1706, illustrated in FIG. 17, the document convertor 338
software program, illustrated in FIG. 3, converts the transferred resume
document to a standardized format. Since a variety of document formats,
such as Microsoft Word and Adobe Portable Document Format (PDF), exist
for candidates to publish their resumes in, the document convertor 338
enables conversion of the content contained within such documents to a
standardized text format in order to facilitate further processing.

[0138] In step 1708, illustrated in FIG. 17, the standardized text
document representing the resume is parsed by a parser software program
340 and an Extensible Markup Language (XML) record for the candidate is
constructed based on the HR-XML resume schema. HR-XML is a library of XML
schemas developed by the HR-XML consortium to support a variety of
business processes related to human resource management. In one
embodiment, the XML record of the candidate includes elements such as
name, contact information, executive summary, technical skills matrix,
projects, education, competencies and references. FIG. 18 illustrates an
exemplary template for the XML record. In alternate embodiments, the XML
record template may be customized based on the context of use. The parser
340 is a software program that scans and analyzes the textual content of
the resume document, and extracts relevant information from the document
in order to populate the fields within the XML record. If the recently
uploaded resume is identified as belonging to an existing candidate, the
candidate's existing XML record is retrieved from the database 120 and is
updated based on the information acquired by parsing the recently
uploaded resume.

[0139] In step 1710, illustrated in FIG. 17, the created/updated XML
record is saved into the database 120 within the multi-source talent
acquisition system 100.

[0140] Referring to FIG. 17, in step 1712, the profile image builder 342
constructs a profile image for the candidate using the candidate's XML
record. Profile image is a two dimensional artifact constructed by the
multi-source talent acquisition system 100 that serves to encapsulate a
holistic representation of the candidate's skills, experience and
qualifications. Profile images play an important role in enabling cluster
representation of candidates on the search results display panel 610 as
will be detailed further on. FIG. 19a illustrates an exemplary profile
image template. An embodiment of the profile image consists of several
pre-defined competency vectors 1902, with each competency vector
consisting of several vector parameters 1904. In the example illustrated
in FIG. 19a, the competency vectors are categorized into the three broad
areas of technical skills 1906, verticals 1908 and roles 1910, and
include vectors representing Java, Oracle, .NET, Finance, Retail,
Healthcare, Architect, Technical Lead and Business Analyst. The example
illustrated in FIG. 19A, further includes vector parameters such as
Number of years 1912, Recency 1914, and Certification 1916. Number of
years 1912 refers to the total number of years of experience the
candidate has with that skill, Recency 1914 refers to how recently the
skill was put to use, and Certification 1916 refers to the number of
certifications in that area. In alternate embodiments, the profile image
template may be customized based on the context of use. The profile image
builder 342 is a software program that scans the candidate's XML record,
extracts relevant information from it and populates the fields within the
profile image template. If the recently uploaded resume is identified as
belonging to an existing candidate, the candidate's existing profile
image is retrieved from the database 120 and is updated based on the
information acquired by parsing the recently uploaded resume. FIG. 19B
illustrates an exemplary profile image for a candidate.

[0141] Returning to FIG. 17, in step 1714, the created/updated profile
image is saved into the database 120 within the multi-source talent
acquisition system 100.

[0142] The multi-source talent acquisition system's database 120 maintains
a multidimensional profile space consisting of profile images, each of
which occupies a point in the multidimensional space. Each axis of the
multidimensional space is characterized by a `competency vector-vector
parameter` combination, with the total number of dimensions being equal
to the total number of `competency vector-vector parameter` combinations
in the profile image template. Each profile image in the multidimensional
space is therefore characterized by a point, the location of which is
determined by the values contained within the profile image. FIG. 19c
illustrates an exemplary profile image for a candidate, whose only
qualification happens to be a certification in Java. In the
multidimensional space, therefore, the profile image of this exemplary
candidate will find a presence on the axis representing
`Java-Certification` since all other values within the profile image are
zero. The multidimensional space is also characterized by clusters of
resources that have similar profiles, since similar vector values
directly implies similar location assignments.

[0143] In step 1716, illustrated in FIG. 17, the multidimensional profile
space is updated by including the newly created/updated profile image in
it.

[0144] FIG. 20 is a flowchart that illustrates the method of computing the
candidate's score as it pertains to the search criteria specified by the
user by using the information contained with candidate's resume. In step
2002, illustrated in FIG. 20, the candidate's XML record and resume are
retrieved from the database 120. In step 2004, illustrated in FIG. 20,
the number of occurrences of each search term within each project in each
year of the candidate's experience is identified from the candidate's XML
record. In step 2006, illustrated in FIG. 20, the number of occurrences
of `star` terms in the proximity of occurrences of each search term in
the candidate's resume is identified. `Star` terms are user-defined words
that are deemed by the user to indicate a degree of superiority of the
skill that they are used in reference to, on the resume. Proximity is
defined as the word-distance range from the search term that the star
terms are to be looked and accounted for. In one embodiment,
`certification` and `certified` may be defined as `star` terms, and the
proximity may be set as 5 words. In this case, the resume would be
scanned to identify occurrences of the terms `certification` and
`certified` within the range of 5 words from each occurrence of a search
term on the candidate's resume. An example of such occurrence in a
candidate's resume, where one of the search terms is Java, and
`certified` is a star term would be `Sun certified Java programmer`. In
alternate embodiments, any term that is deemed to reflect a superior
knowledge of the search term solely by the proximity of its presence to
the search term may be defined as a `star` term.

[0145] Returning to FIG. 20, in step 2008, for each project on the
candidate's XML record where there is an occurrence of the search term,
Recency Factor is computed as follows:

RF j = MRF - ( CY - Y j CY - OY ) ##EQU00007##

[0146] where MRF is `Maximum Recency Factor`, CY is the current year, Yj
is the end-year of the project for which the `Recency Factor` RFj is
being computed, and OY is the end-year of the oldest project in context
in which there is an occurrence of the specific search term. The value of
the Maximum Recency Factor is user configurable, subject to a minimum
value of `2`.

[0147] In step 2010, illustrated in FIG. 20, the candidate's resume score
for each search term is computed as:

s = [ i j ( RF j × N ij ) ] × [ 1
+ k ( PF k × Occ k ) ] ##EQU00008##

[0148] where `i` is each year under consideration, `j` is each project
occurring in a given year, RFj is the search engine computed Recency
Factor for the project in context for the specific occurrence of the
search term, Nij is the number of occurrences of the search term within
the year and project in context, PFk is the user-defined Proximity Factor
for each star term, and Occk is the number of occurrences of the search
term within proximity of the specific star-term.

[0149] In step 2012, illustrated in FIG. 20, the candidate resume score is
computed for each search term specified by the user in the search
criteria entry field 602, using the method illustrated in step 2010.

[0150] In steps 2014 and 2016, illustrated in FIG. 20, the weights
specified by the user for each search term is retrieved, and the total
candidate resume score across all search terms is computed as:

S = k = 1 n ( s k × w k ) ##EQU00009##

[0151] where `n` is the total number of user specified search terms, sk is
the candidate resume score for a specific search term, and wk is the user
specified weight for the specific search term.

[0152] Search Results Display Phase

[0153] Following computation of candidate scores based on the search
criteria specified by the user and the source selected by the user,
matching candidates are displayed on the search results display panel
610, illustrated in FIG. 6. Each matching candidate is represented by
means of a candidate object 612a, as illustrated in FIG. 6. In one
embodiment, spheres labeled with the names of candidates are used as
candidate objects. In alternate embodiments, any graphical shape/element
may be used as candidate objects. In one embodiment, a gradient
background is used on the search results display panel 610, and candidate
objects are positioned on the gradient display based on the scores with
the highest scorers being placed closer toward the center. The distance
of a candidate object from the center of the display is a direct visual
indicator of the level of match of the represented candidate with the
search criteria. In another embodiment, candidate objects representing
similar candidates are clustered together on the search results display
panel 610. The level of similarity between two matching candidates to be
displayed on the search results display panel 610 is derived by the
distance between the profile images representing the two candidates in
the multidimensional profile space. Since candidates with similar
profiles tend to have similar profile images and hence be within close
proximity in the multidimensional profile space, the candidate objects
representing them on the search results display panel 610 will be
clustered together. An embodiment of the search results display panel,
therefore, enables the user to not only visualize the relevancy of a
candidate to the indicated search criteria, but also visualize the
similarities between candidates returned as a result of the search.

[0154] In one embodiment, a pre-set number of candidate objects alone are
displayed on the search results display panel 610 irrespective of the
total number of candidates that are identified as matching the search
criteria. After having reviewed the candidates displayed on the search
results display panel 610, should the user wish to view more candidates,
the user zooms-out or pans using the zoom/pan control 626 to enable a
higher level view of the search results display panel 610. This will
result in more candidate objects coming into view on the search results
display panel 610. The user may use the zoom/pan control 626 any number
of times after a search is executed in order to control the number of
candidate objects being displayed on the search results display panel
610.

[0155] Referring to FIG. 6, a summary of the search results is displayed
in the search summary display panel 616. The information displayed in the
search summary display panel 616 includes `number of candidates
searched`, `number of candidates that match the search criteria from
amongst those searched`, `number of sources searched`, and a graphical
chart to represent the number of matching candidates for each component
of the search criteria.

[0156] Further in reference to FIG. 6, when the user places the mouse
pointer over a candidate object 612c in the search results display panel
610, a profile snapshot window 614 pops open. The profile snapshot window
614 displays the candidate's name, location, contact details,
availability, score, photo, and buttons for profile display and test
scheduling. Information pertaining to the candidate displayed on the
profile snapshot window 614 is procured by the multi-source talent
acquisition system 100 from the candidate information system 108.

[0157] When the user clicks on a candidate object 612a, information
pertaining to the candidate represented by the candidate object 612a gets
displayed on the candidate profile display panel 618, candidate
synopsis/skills display panel 620, and the candidate score/report display
panel 622. In one embodiment, the candidate profile display panel 618
includes information such as candidate's name, location, contact details,
video profile, availability status, and links to external websites that
carry more information about the candidate. Alternate embodiments will
offer the ability to customize the information displayed in this panel.
Information pertaining to the candidate displayed on the candidate
profile display panel 618 is procured by the multi-source talent
acquisition system 100 from the candidate information system 108.

[0158] FIGS. 21a and 21b illustrate exemplary views of the synopsis/skills
display panel 620. In one embodiment, the default view is as illustrated
in FIG. 21a, where the user is displayed a skills matrix 2102 consisting
of the number of years of experience, recency and number of
certifications for each of the skills in the search criteria. When the
user places the mouse pointer over an item representing the number of
certifications 2104, a window pops open listing details about the
certification(s). In one embodiment, this includes details such as
certification name, name of the certifying agency, and date until which
the certification is valid. When the user clicks on the synopsis button
2106, the synopsis/skills display panel 620 toggles view as shown in FIG.
21b to display the candidate's professional summary 2112. The user may
toggle back to the skills view by clicking on the skills button 2114
illustrated in FIG. 21b. When the user clicks on the profile image button
2108, a profile image display window opens up to display the profile
image of the selected candidate. FIG. 22 illustrates an exemplary profile
image display window. When the user clicks on the resume button 2110, a
resume display window opens up to display the candidate's resume. FIG. 23
illustrates an exemplary resume display window. An embodiment of the
resume display window also enables the user to download the resume in a
variety of formats.

[0159] FIG. 24 illustrates a closer view of the score/report display panel
622. In one embodiment the score/report display panel includes a
histogram 2402 that shows the selected candidate's score position amongst
the scores of other matching candidates, and a pie-chart 2404 showing
distribution of scores amongst the search terms for the selected
candidate. When the user clicks on the report button 2406 in the
score/report display panel 622 illustrated in FIG. 24, a report display
window opens up. FIG. 25 illustrates an exemplary report display window
2502. In one embodiment, the report display window 2502 includes
histogram 2504 showing selected candidate's score position amongst the
scores of other matching candidates, pie-chart 2506 showing distribution
of scores amongst the search terms for the selected candidate, chart
comparing selected candidate's resume score with maximum resume score,
minimum resume score, and average resume score 2508, search term summary
including year of most recent use, and number of years of use 2510,
histogram 2512 showing selected candidate's score position amongst the
scores of other matching candidates for each search term, chart 2514
comparing selected candidate's resume score with maximum resume score,
minimum resume score, and average resume score for each search term.

[0160] The user may shortlist a candidate for further assessment, by
selecting a candidate object 612a representing a candidate, and clicking
on the schedule test button located in the shortlisted candidates panel
624. In another embodiment, the user may also add candidates to the list
in the shortlisted candidates panel 624 by clicking on the `add to
schedule test` button in the profile snapshot window 614 that pops up
while placing the mouse pointer over a candidate object. Information
regarding the shortlisted candidates is transmitted by the multi-source
talent acquisition system 100 to the test system 112 and/or the interview
system 114 for scheduling and administration.

[0161] While the embodiments of the invention disclosed herein are
presently considered to be preferred, various changes and modifications
can be made without departing from the spirit and scope of the invention.
The scope of the invention is indicated in the appended claims, and all
changes that come within the meaning and range of equivalents are
intended to be embraced therein.